{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "65_Qg86Sl2eF"
   },
   "source": [
    "# Basic Semantic Search\n",
    "Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search. \n",
    "\n",
    "![Searching an archive using sentence embeddings](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/basic-semantic-search-overview.png?3)\n",
    "\n",
    "In this notebook, we'll build a simple semantic search engine. The applications of semantic search go beyond building a web search engine. They can empower a private search engine for internal documents or records. It can also be used to power features like StackOverflow's \"similar questions\" feature.\n",
    "\n",
    "1. Get the archive of questions\n",
    "2. [Embed](https://docs.cohere.ai/embed-reference/) the archive\n",
    "3. Search using an index and nearest neighbor search\n",
    "4. Visualize the archive based on the embeddings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PUaWQA1znD_g",
    "outputId": "6319482e-7186-42a7-b7e4-bcaa4ff58c32",
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Install Cohere for embeddings, Umap to reduce embeddings to 2 dimensions, \n",
    "# Altair for visualization, Annoy for approximate nearest neighbor search\n",
    "# TODO: upgrade to \"cohere>5\"",
"!pip install \"cohere<5\" umap-learn altair annoy datasets tqdm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you're running an older version of the SDK, you might need to upgrade it like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade cohere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5qXUv-UqvWcw"
   },
   "source": [
    "Get your Cohere API key by [signing up here](https://os.cohere.ai/register). Paste it in the cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Getting Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "pgh8_8qMMnqp"
   },
   "outputs": [],
   "source": [
    "#@title Import libraries (Run this cell to execute required code) {display-mode: \"form\"}\n",
    "\n",
    "import cohere\n",
    "import numpy as np\n",
    "import re\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from datasets import load_dataset\n",
    "import umap\n",
    "import altair as alt\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from annoy import AnnoyIndex\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll need your API key for this next cell. [Sign up to Cohere](https://os.cohere.ai/) and get one if you haven't yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll set up the name of the model we want to use, the API key, and the input type.\n",
    "# Create and retrieve a Cohere API key from dashboard.cohere.ai/welcome/register\n",
    "# Paste your API key here. Remember to not share publicly\n",
    "model_name = \"embed-english-v3.0\"\n",
    "api_key = \"\"\n",
    "input_type_embed = \"search_document\"\n",
    "\n",
    "# Now we'll set up the cohere client.\n",
    "co = cohere.Client(api_key)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "2LZ7SBKSU4bx"
   },
   "source": [
    "## 2. Get The Archive of Questions\n",
    "We'll use the [trec](https://www.tensorflow.org/datasets/catalog/trec) dataset which is made up of questions and their categories."
   ]
  },
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    "outputId": "9717e298-5c90-49ed-9aff-ae4156b3beef"
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   "outputs": [
    {
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       "   label-coarse  label-fine  \\\n",
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       "                                                                   text  \n",
       "0                    How did serfdom develop in and then leave Russia ?  \n",
       "1                      What films featured the character Popeye Doyle ?  \n",
       "2                   How can I find a list of celebrities ' real names ?  \n",
       "3  What fowl grabs the spotlight after the Chinese Year of the Monkey ?  \n",
       "4                                       What is the full form of .com ?  \n",
       "5            What contemptible scoundrel stole the cork from my lunch ?  \n",
       "6                   What team did baseball 's St. Louis Browns become ?  \n",
       "7                                       What is the oldest profession ?  \n",
       "8                                              What are liver enzymes ?  \n",
       "9                   Name the scar-faced bounty hunter of The Old West .  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get dataset\n",
    "dataset = load_dataset(\"trec\", split=\"train\")\n",
    "\n",
    "# Import into a pandas dataframe, take only the first 1000 rows\n",
    "df = pd.DataFrame(dataset)[:1000]\n",
    "\n",
    "# Preview the data to ensure it has loaded correctly\n",
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "zrajiGkNVw7E"
   },
   "source": [
    "## 2. Embed the archive\n",
    "The next step is to embed the text of the questions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![embedding archive texts](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/semantic-search-embed-text-archive.png)\n",
    "\n",
    "To get a thousand embeddings of this length should take about fifteen seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AD7VSg88Yc75",
    "outputId": "1bcaf665-db76-41e2-e960-70dfdd5e23b3",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get the embeddings\n",
    "embeds = co.embed(texts=list(df['text']),\n",
    "                  model=model_name,\n",
    "                  input_type=input_type_embed).embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TgG_l10YY2rH",
    "outputId": "6e34a8a1-0af2-4623-943a-db2f6741afda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4096)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dimensions of the embeddings\n",
    "embeds = np.array(embeds)\n",
    "embeds.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "lzcDFCDePVxA"
   },
   "source": [
    "## 3. Search using an index and nearest neighbor search\n",
    "![Building the search index from the embeddings](https://github.com/cohere-ai/notebooks/raw/main/notebooks/images/semantic-search-index.png)\n",
    "Let's now use [Annoy](https://github.com/spotify/annoy) to build an index that stores the embeddings in a way that is optimized for fast search. This approach scales well to a large number of texts (other options include [Faiss](https://github.com/facebookresearch/faiss), [ScaNN](https://github.com/google-research/google-research/tree/master/scann), and [PyNNDescent](https://github.com/lmcinnes/pynndescent)).\n",
    "\n",
    "After building the index, we can use it to retrieve the nearest neighbors either of existing questions (section 3.1), or of new questions that we embed (section 3.2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fPCuyFc8w04q",
    "outputId": "7e7a0491-d279-4998-f01b-63466947057b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the search index, pass the size of embedding\n",
    "search_index = AnnoyIndex(embeds.shape[1], 'angular')\n",
    "# Add all the vectors to the search index\n",
    "for i in range(len(embeds)):\n",
    "    search_index.add_item(i, embeds[i])\n",
    "\n",
    "search_index.build(10) # 10 trees\n",
    "search_index.save('test.ann')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "GfIDv25K0A-0"
   },
   "source": [
    "### 3.1. Find the neighbors of an example from the dataset\n",
    "If we're only interested in measuring the distance between the questions in the dataset (no outside queries), a simple way is to calculate the distance between every pair of embeddings we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "id": "AuBw_S7TyUiS",
    "outputId": "6ae857c6-dd8e-499f-bb4c-6516b317c8b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question:'What are bear and bull markets ?'\n",
      "Nearest neighbors:\n"
     ]
    },
    {
     "data": {
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       "      <th>614</th>\n",
       "      <td>What animals do you find in the stock market ?</td>\n",
       "      <td>0.904278</td>\n",
       "    </tr>\n",
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       "      <th>137</th>\n",
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       "      <td>0.992819</td>\n",
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       "      <td>1.080738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>What does it mean `` Rupee Depreciates '' ?</td>\n",
       "      <td>1.086724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>Why did the world enter a global depression in 1929 ?</td>\n",
       "      <td>1.099370</td>\n",
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       "    <tr>\n",
       "      <th>547</th>\n",
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       "      <td>1.105368</td>\n",
       "    </tr>\n",
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       "      <td>What is the difference between a median and a mean ?</td>\n",
       "      <td>1.141870</td>\n",
       "    </tr>\n",
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       "      <th>601</th>\n",
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      "text/plain": [
       "                                                     texts  distance\n",
       "614         What animals do you find in the stock market ?  0.904278\n",
       "137                           What are equity securities ?  0.992819\n",
       "513                                What do economists do ?  1.066583\n",
       "307                           What does NASDAQ stand for ?  1.080738\n",
       "363            What does it mean `` Rupee Depreciates '' ?  1.086724\n",
       "932  Why did the world enter a global depression in 1929 ?  1.099370\n",
       "547                   Where can stocks be traded on-line ?  1.105368\n",
       "922   What is the difference between a median and a mean ?  1.141870\n",
       "601                      What is `` the bear of beers '' ?  1.154140"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose an example (we'll retrieve others similar to it)\n",
    "example_id = 92\n",
    "\n",
    "# Retrieve nearest neighbors\n",
    "similar_item_ids = search_index.get_nns_by_item(example_id,10,\n",
    "                                                include_distances=True)\n",
    "# Format and print the text and distances\n",
    "results = pd.DataFrame(data={'texts': df.iloc[similar_item_ids[0]]['text'], \n",
    "                             'distance': similar_item_ids[1]}).drop(example_id)\n",
    "\n",
    "print(f\"Question:'{df.iloc[example_id]['text']}'\\nNearest neighbors:\")\n",
    "results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "bI4kGnnPcu90"
   },
   "source": [
    "### 3.2. Find the neighbors of a user query\n",
    "We're not limited to searching using existing items. If we get a query, we can embed it and find its nearest neighbors from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "-eogJaFOyxt0",
    "outputId": "94f9900d-2f0d-4c4b-fc31-1aa9187259b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query:'What is the tallest mountain in the world?'\n",
      "Nearest neighbors:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>texts</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>What is the name of the tallest mountain in the world ?</td>\n",
       "      <td>0.447309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>What is the highest mountain in the world ?</td>\n",
       "      <td>0.552254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>What was the highest mountain on earth before Mount Everest was discovered ?</td>\n",
       "      <td>0.801252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>What mountain range is traversed by the highest railroad in the world ?</td>\n",
       "      <td>0.929516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>What is the highest peak in Africa ?</td>\n",
       "      <td>0.930806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Where is the highest point in Japan ?</td>\n",
       "      <td>0.977315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>What 's the longest river in the world ?</td>\n",
       "      <td>1.064209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>What is the largest snake in the world ?</td>\n",
       "      <td>1.076390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>What 's the second-largest island in the world ?</td>\n",
       "      <td>1.088034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>What is the highest waterfall in the United States ?</td>\n",
       "      <td>1.091145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                            texts  \\\n",
       "236                       What is the name of the tallest mountain in the world ?   \n",
       "670                                   What is the highest mountain in the world ?   \n",
       "412  What was the highest mountain on earth before Mount Everest was discovered ?   \n",
       "907       What mountain range is traversed by the highest railroad in the world ?   \n",
       "435                                          What is the highest peak in Africa ?   \n",
       "109                                         Where is the highest point in Japan ?   \n",
       "901                                      What 's the longest river in the world ?   \n",
       "114                                      What is the largest snake in the world ?   \n",
       "962                              What 's the second-largest island in the world ?   \n",
       "27                           What is the highest waterfall in the United States ?   \n",
       "\n",
       "     distance  \n",
       "236  0.447309  \n",
       "670  0.552254  \n",
       "412  0.801252  \n",
       "907  0.929516  \n",
       "435  0.930806  \n",
       "109  0.977315  \n",
       "901  1.064209  \n",
       "114  1.076390  \n",
       "962  1.088034  \n",
       "27   1.091145  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"What is the tallest mountain in the world?\"\n",
    "input_type_query = \"search_query\"\n",
    "\n",
    "# Get the query's embedding\n",
    "query_embed = co.embed(texts=[query],\n",
    "                  model=model_name,\n",
    "                  input_type=input_type_query).embeddings\n",
    "\n",
    "# Retrieve the nearest neighbors\n",
    "similar_item_ids = search_index.get_nns_by_vector(query_embed[0],10,\n",
    "                                                include_distances=True)\n",
    "# Format the results\n",
    "query_results = pd.DataFrame(data={'texts': df.iloc[similar_item_ids[0]]['text'], \n",
    "                             'distance': similar_item_ids[1]})\n",
    "\n",
    "\n",
    "print(f\"Query:'{query}'\\nNearest neighbors:\")\n",
    "print(query_results) # NOTE: Your results might look slightly different to ours."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "4tN7fGDst7rh"
   },
   "source": [
    "## 4. Visualizing the archive\n",
    "Finally, let's plot out all the questions onto a 2D chart so you're able to visualize the semantic similarities of this dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "id": "BpWn78FwZ70f",
    "outputId": "71ac3484-54c2-4689-9b16-1a1d57b3243f"
   },
   "outputs": [
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\"What 19th-century painter died in the Marquesas Islands ?\", \"x\": 7.112317085266113, \"y\": 1.2610114812850952}, {\"text\": \"The corpus callosum is in what part of the body ?\", \"x\": 5.717758655548096, \"y\": 5.891611576080322}, {\"text\": \"How do I log on to home page at Headquarters U.S. European Command ?\", \"x\": 9.507502555847168, \"y\": 6.028642654418945}, {\"text\": \"What country did the Nazis occupy for 1 , CD NNS IN NNP NNP NNP .\", \"x\": 8.649563789367676, \"y\": 2.011504888534546}, {\"text\": \"What is Nebraska 's most valuable resource ?\", \"x\": 6.900418758392334, \"y\": 2.7707419395446777}, {\"text\": \"How many species of the Great White shark are there ?\", \"x\": 5.671431064605713, \"y\": 3.9457476139068604}, {\"text\": \"What is the origin of head lice ?\", \"x\": 5.53376579284668, \"y\": 6.15777063369751}, {\"text\": \"What is the nickname for the state of Mississippi ?\", \"x\": 6.575149059295654, \"y\": 3.1269216537475586}, {\"text\": \"What was the first domesticated bird ?\", \"x\": 5.6867194175720215, \"y\": 4.715795993804932}, {\"text\": \"How many villi are found in the small intestine ?\", \"x\": 6.658392429351807, \"y\": 4.5131354331970215}, {\"text\": \"What country did King Wenceslas rule ?\", \"x\": 7.869290351867676, \"y\": 1.3130402565002441}, {\"text\": \"What is a fear of shadows ?\", \"x\": 5.240066051483154, \"y\": 6.735344409942627}, {\"text\": \"Who were the five Marx brothers ?\", \"x\": 11.170981407165527, \"y\": 3.4061224460601807}, {\"text\": \"In which year was New Zealand excluded from the ANZUS alliance ?\", \"x\": 8.76473331451416, \"y\": 1.8043838739395142}, {\"text\": \"When did CNN begin broadcasting ?\", \"x\": 9.28637981414795, \"y\": 2.214888334274292}, {\"text\": \"What causes the body to shiver in cold temperatures ?\", \"x\": 5.8806915283203125, \"y\": 6.039300918579102}, {\"text\": \"What are bear and bull markets ?\", \"x\": 8.009658813476562, \"y\": 6.0985822677612305}, {\"text\": \"What 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    "#@title Plot the archive {display-mode: \"form\"}\n",
    "\n",
    "# UMAP reduces the dimensions from 1024 to 2 dimensions that we can plot\n",
    "reducer = umap.UMAP(n_neighbors=20) \n",
    "umap_embeds = reducer.fit_transform(embeds)\n",
    "# Prepare the data to plot and interactive visualization\n",
    "# using Altair\n",
    "df_explore = pd.DataFrame(data={'text': df['text']})\n",
    "df_explore['x'] = umap_embeds[:,0]\n",
    "df_explore['y'] = umap_embeds[:,1]\n",
    "\n",
    "# Plot\n",
    "chart = alt.Chart(df_explore).mark_circle(size=60).encode(\n",
    "    x=#'x',\n",
    "    alt.X('x',\n",
    "        scale=alt.Scale(zero=False)\n",
    "    ),\n",
    "    y=\n",
    "    alt.Y('y',\n",
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    "    ),\n",
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    "Hover over the points to read the text. Do you see some of the patterns in clustered points? Similar questions, or questions asking about similar topics?\n",
    "\n",
    "This concludes this introductory guide to semantic search using sentence embeddings. As you continue the path of building a search product additional considerations arise (like dealing with long texts, or finetuning to better improve the embeddings for a specific use case). \n",
    "\n",
    "\n",
    "We can’t wait to see what you start building! Share your projects or find support on [Discord](https://discord.com/invite/co-mmunity).\n"
   ]
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